Demonstrates appending millions of points to a line chart with SciChart.js, High Performance JavaScript Charts
drawExample.ts
index.html
RandomWalkGenerator.ts
vanilla.ts
theme.ts
1import { appTheme } from "../../../theme";
2import { RandomWalkGenerator } from "../../../ExampleData/RandomWalkGenerator";
3
4import {
5 EAutoRange,
6 EDragMode,
7 FastLineRenderableSeries,
8 MouseWheelZoomModifier,
9 NumericAxis,
10 RubberBandXyZoomModifier,
11 SciChartSurface,
12 XAxisDragModifier,
13 XyDataSeries,
14 YAxisDragModifier,
15 ZoomExtentsModifier,
16} from "scichart";
17
18export const drawExample = async (rootElement: string | HTMLDivElement) => {
19 // Define some constants
20 const numberOfPointsPerTimerTick = 1000; // 1,000 points every timer tick
21 const timerInterval = 10; // timer tick every 10 milliseconds
22 const maxPoints = 1e6; // max points for a single series before the demo stops
23
24 // Create a SciChartSurface
25 // Note create() uses shared WebGL canvas, createSingle() uses one WebGL per chart
26 // createSingle() = faster performance as doesn't require a copy-op, but limited by max-contexts in browser
27 const { wasmContext, sciChartSurface } = await SciChartSurface.createSingle(rootElement, {
28 theme: appTheme.SciChartJsTheme,
29 });
30
31 // Create an XAxis and YAxis with autoRange=Always
32 const xAxis = new NumericAxis(wasmContext, { autoRange: EAutoRange.Always });
33 sciChartSurface.xAxes.add(xAxis);
34 const yAxis = new NumericAxis(wasmContext, { autoRange: EAutoRange.Always });
35 sciChartSurface.yAxes.add(yAxis);
36
37 // Create some DataSeries
38 const dataSeries: XyDataSeries[] = [
39 new XyDataSeries(wasmContext, { containsNaN: false, isSorted: true }),
40 new XyDataSeries(wasmContext, { containsNaN: false, isSorted: true }),
41 new XyDataSeries(wasmContext, { containsNaN: false, isSorted: true }),
42 ];
43
44 const seriesColors = [appTheme.VividSkyBlue, appTheme.VividOrange, appTheme.VividPink];
45
46 // Create some FastLineRenderableSeries bound to each dataSeries and add to the chart
47 dataSeries.map((ds, index) => {
48 sciChartSurface.renderableSeries.add(
49 new FastLineRenderableSeries(wasmContext, {
50 dataSeries: ds,
51 strokeThickness: 2,
52 stroke: seriesColors[index],
53 })
54 );
55 });
56
57 // Add some interactivity modifiers. These are only operational when the demo is paused
58 // as interactivity conflicts with AutoRange.Always
59 sciChartSurface.chartModifiers.add(
60 new RubberBandXyZoomModifier(),
61 new MouseWheelZoomModifier(),
62 new XAxisDragModifier({ dragMode: EDragMode.Panning }),
63 new YAxisDragModifier({ dragMode: EDragMode.Panning }),
64 new ZoomExtentsModifier()
65 );
66
67 // This class generates some data for our example
68 // It generates a random walk, which is a line which increases or decreases by a random value
69 // each data-point
70 const randomWalkGenerators = [1, 2, 3].map((_) => {
71 return new RandomWalkGenerator(0);
72 });
73
74 let timerId: NodeJS.Timeout;
75
76 // Function called when the user clicks stopUpdate button
77 const stopUpdate = () => {
78 clearTimeout(timerId);
79 timerId = undefined;
80 randomWalkGenerators.forEach((rw) => rw.reset());
81 // Disable autoranging on X when the demo is paused. This allows zooming and panning
82 xAxis.autoRange = EAutoRange.Once;
83 };
84
85 // Function called when the user clicks startUpdate button
86 const startUpdate = () => {
87 // // tslint:disable-next-line:no-debugger
88 // debugger;
89 if (timerId) {
90 stopUpdate();
91 }
92 const updateFunc = () => {
93 if (dataSeries[0].count() >= maxPoints) {
94 stopUpdate();
95 return;
96 }
97
98 randomWalkGenerators.forEach((randomWalk, index) => {
99 // Get the next N random walk x,y values
100 const { xValues, yValues } = randomWalk.getRandomWalkSeries(numberOfPointsPerTimerTick);
101
102 // Append these to the dataSeries. This will cause the chart to redraw
103 dataSeries[index].appendRange(xValues, yValues);
104 });
105
106 timerId = setTimeout(updateFunc, timerInterval);
107 };
108
109 // Enable autoranging on X when running the demo
110 xAxis.autoRange = EAutoRange.Always;
111
112 dataSeries.forEach((ds) => ds.clear());
113
114 timerId = setTimeout(updateFunc, timerInterval);
115 };
116
117 type TRenderStats = { numberPoints: number; fps: number };
118 type TOnRenderStatsChangeCallback = (stats: TRenderStats) => void;
119
120 let statsCallback: TOnRenderStatsChangeCallback = () => {};
121 const setStatsChangedCallback = (callback: TOnRenderStatsChangeCallback) => {
122 statsCallback = callback;
123 };
124
125 let lastRendered = Date.now();
126 sciChartSurface.renderedToDestination.subscribe(() => {
127 const currentTime = Date.now();
128 const timeDiffSeconds = new Date(currentTime - lastRendered).getTime() / 1000;
129 lastRendered = currentTime;
130 const fps = 1 / timeDiffSeconds;
131 const renderStats = {
132 numberPoints:
133 sciChartSurface.renderableSeries.size() * sciChartSurface.renderableSeries.get(0).dataSeries.count(),
134 fps,
135 };
136
137 statsCallback(renderStats);
138 });
139
140 return { wasmContext, sciChartSurface, controls: { startUpdate, stopUpdate, setStatsChangedCallback } };
141};
142This example, “JavaScript Chart Performance Demo”, demonstrates how to create a highly performant real-time chart using plain JavaScript and SciChart.js. The demo continuously appends large batches of data points to a line chart while measuring performance metrics such as frames per second (FPS), all powered by the efficient WebGL rendering capabilities of SciChart.js.
The implementation begins by asynchronously initializing a SciChartSurface using the dedicated WebGL canvas via the SciChartSurface.createSingle() method (see Adding Realtime Updates | JavaScript Chart Documentation). Two NumericAxis are created and configured with auto-ranging set to EAutoRange.Always, which is detailed in the Axis Ranging - AutoRange Documentation. Three XyDataSeries objects are instantiated and bound to FastLineRenderableSeries to efficiently render large ranges of data points. A setTimeout loop is used to drive the continuous, real-time data updates, with a RandomWalkGenerator appending 1,000 points every 10 milliseconds. Performance is measured by subscribing to the sciChartSurface.rendered event, which calculates metrics such as FPS and the total number of data points - techniques discussed in the Performance Tips & Tricks Guide.
Real-Time Updates: The demo showcases live data streaming by continuously appending up to millions of data points. This demonstrates how efficiently DataSeries Realtime Updates can be implemented to handle high-frequency updates in JavaScript Charts with SciChart.
Axis Configuration: By using NumericAxis with an auto-range configuration, the chart automatically adjusts its view to new data without manual intervention. Developers interested in further details can review the Axis Ranging - AutoRange Documentation.
Asynchronous Initialization: The use of async/await ensures that the chart is set up efficiently and that resources are properly managed. This practice follows guidelines outlined in the Getting Started with SciChart JS documentation.
Performance Measurement: The rendered.subscribe event is leveraged to compute performance statistics like FPS, enabling developers to gain insights into rendering efficiency. For more on these techniques, see the Performance Tips & Tricks Guide.
Even though this example is implemented purely in JavaScript, the control patterns—such as starting/stopping real-time updates and cleaning up resources using a destructor function—are applicable to any framework. The chart controls provide methods to start and stop the update loop, and the SciChartSurface.delete() method is called to dispose of WebGL resources efficiently, aligning with the best practices detailed in the Memory Best Practices documentation. This example clearly illustrates how real-time chart updates, efficient data series handling, and performance measurement techniques can be combined in a JavaScript environment using SciChart.js.

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